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Let’s talk about the weather: a cluster-based approach to weather forecast accuracy

Author

Listed:
  • Jill F. Lundell

    (Department of Mathematics and Statistics Utah State University)

  • Brennan Bean

    (Department of Mathematics and Statistics Utah State University)

  • Jürgen Symanzik

    (Department of Mathematics and Statistics Utah State University)

Abstract

Improved understanding of characteristics related to weather forecast accuracy in the United States may help meteorologists develop more accurate predictions and may help Americans better interpret their daily weather forecasts. This article examines how spatio-temporal characteristics across the United States relate to forecast accuracy. We cluster the United States into six weather regions based on weather and geographic characteristics and analyze the patterns in forecast accuracy within each weather region. We then explore the relationship between climate characteristics and forecast accuracy within these weather regions. We conclude that patterns in forecast errors are closely related to the unique climates that characterize each region.

Suggested Citation

  • Jill F. Lundell & Brennan Bean & Jürgen Symanzik, 2023. "Let’s talk about the weather: a cluster-based approach to weather forecast accuracy," Computational Statistics, Springer, vol. 38(3), pages 1135-1155, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-023-01339-3
    DOI: 10.1007/s00180-023-01339-3
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    References listed on IDEAS

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    1. Judah Cohen & Karl Pfeiffer & Jennifer A. Francis, 2018. "Warm Arctic episodes linked with increased frequency of extreme winter weather in the United States," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    2. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    3. Wickham, Hadley, 2007. "Reshaping Data with the reshape Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i12).
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